In part 2 of this series, we got started on the various menus in Stata. This post covers an important menu that you’ll probably use often: the graphics menu.
What’s in the Graphics menu
The graphics menu provides an impressive variety of options for creating just about any graph you might need.
Take a look at the menu. It includes everything from univariate graphs like bar charts and pie charts to more complex, multivariate plots. Go ahead and explore some of the graphs available in the menu.
A comprehensive resource for a full understanding of the graphics you can do in Stata is the Stata Graphics Reference Manual, which is a free pdf download from the Stata web site. At nearly 800 pages, though, it’s not a quick read (it is excellent, though!).
A much quicker read is the Stata Data Visualization Cheat Sheet. Pages 5 – 6.
Browsing this two-page resource will tell you a lot about what you can do in Stata graphics. This includes not only which kinds of graphs you can create, but how to customize a graph’s appearance, apply themes, and save plots.
But first let’s explore how easy it is to create a simple, but customized plot using only the menus.
An Example of creating a Scatter plot using menus
To show an example, we’ll use the auto data. If you haven’t loaded up the data in your current session, type the 
following into your command line
sysuse auto
Note that you could also open this data set using the File menu, but this is a command that is so simple, it’s faster to just type it into the command line.
As you’ll see, every time you use the menus, Stata fills in the associated commands for you into the command line
Now say we want to make a scatter plot with price on the y axis, and mpg on the x axis, but only for observations where the gear ratio is less than 3. We want this graph to have red triangles representing points, and we want it to have informative titles:
We click on Graphics -> Twoway graph. In the plots window click Create and select Basic plots -> Scatter.
Choose price as the y variable and mpg as the x variable; don’t press accept yet.
Under Marker properties choose Triangle as the symbol and Red as the color. Also notice you can also change the size or opacity of points or mark particular observations.
Click accept, then click accept on the next page.
Under the if/in tab, type “gear_ratio<3” so only observations with a gear ratio less than 3 are plotted; click on Y axis.
Under the Y axis tab type the title “Price”, and under the X axis tab type the title “MPG”. Note how you can also change properties of the axis.
In the Titles tab type an appropriate title for the graph – I chose “Price and Mileage”.
We don’t want to see a legend so in the Legend tab choose “hide legend”.
You can now press ok and should see the following graph:
You’ll also see that Stata put this code put to the console:
twoway (scatter price mpg, mcolor(red) msymbol(triangle)) if gear_ratio<3, ytitle(`"Price"') xtitle(`"MPG"') title(`"Price and Mileage"') legend(off)
Now you know how to make this graph and similar ones with syntax as well! If you’re ever having trouble creating a certain chart using code, the graph menu can provide an easier way to select the options you want.
Note: when you make plots in Stata menus, make sure to always make a new plot rather than layering on an old one. If you press create when you already have a plot selected, your new scatterplot will layer on top of the old one.
by James Harrod
About the Author:
James Harrod interned at The Analysis Factor in the summer of 2023. He plans to continue into a career as an actuary, and hopes to continue finding interesting ways of educating people about statistics. James is well-versed in R and Stata programming and enjoys teaching the intuition behind common statistical methods. James is a 2023 graduate of the University of Rochester with bachelor’s degrees in Statistics and Economics.
A great way to get started with Stata is using its menus.
The first part of this Tutorial Series introduced you to Stata’s windows. You can now begin learning how to use Stata to work with data.
Across the top are 8 tabs: File, Edit, Data, Graphics, Statistics, User, Window, and Help.
We will not go through every option within the Stata menus. Instead, we’ll highlight a few options to get you started. In this article, we’ll start with three of the most useful menus: File, Data, and Help, along with those helpful icons under the menus.
In our next article, we’ll look at two more: Graphics and Statistics.
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So, you want to get started with Stata?
Good choice!
At The Analysis Factor we recommend first becoming proficient in one statistical software. Then once you’ve progressed up to learning Stage 3 skills, adding a second statistical software. Whether it’s your first, second, or 5th statistical software, Stata has a lot that makes it worth learning.
When I first started using Stata, I remember being confused by the variety of menus and windows, the strange syntax of the code, the way it handled datasets… and what the heck is a do file? (more…)
One big advantage of R is its breadth. If anything has been done in statistics, there is an R package that will do it.
The problem is that sometimes there are four packages that will do it. This is big problem with R (and with Python for that matter). (more…)
Many data sets are challenging and time consuming to work with because the data are seldom in an optimal format.
(more…)
If you have a categorical predictor variable that you plan to use in a regression analysis in SPSS, there are a couple ways to do it.
You can use the SPSS Regression procedure. Or you can use SPSS General Linear Model–>Univariate, which I discuss here. If you use Syntax, it’s the UNIANOVA command.
The big question in SPSS GLM is what goes where. As I’ve detailed in another post, any continuous independent variable goes into covariates. And don’t use random factors at all unless you really know what you’re doing.
So the question is what to do with your categorical variables. You have two choices, and each has advantages and disadvantages.
The easiest is to put categorical variables in Fixed Factors. SPSS GLM will dummy code those variables for you, which is quite convenient if your categorical variable has more than two categories.
However, there are some defaults you need to be aware of that may or may not make this a good choice.
The dummy coding reference group default
SPSS GLM always makes the reference group the one that comes last alphabetically.
So if the values you input are strings, it will be the one that comes last. If those values are numbers, it will be the highest one.
Not all procedures in SPSS use this default so double check the default if you’re using something else. Some procedures in SPSS let you change the default, but GLM doesn’t.
In some studies it really doesn’t matter which is the reference group.
But in others, interpreting regression coefficients will be a whole lot easier if you choose a group that makes a good comparison such as a control group or the most common group in the data.
If you want that to be the reference group in SPSS GLM, make it come last alphabetically. I’ve been known to do things like change my data so that the control group becomes something like ZControl. (But create a new variable–never overwrite original data).
It really can get confusing, though, if the variable was already dummy coded–if it already had values of 0 and 1. Because 1 comes last alphabetically, SPSS GLM will make that group the reference group and internally code it as 0.
This can really lead to confusion when interpreting coefficients. It’s not impossible if you’re paying attention, but you do have to pay attention. It’s generally better to recode the variable so that you don’t confuse yourself. And while you may believe you’re up for overcoming the confusion, why make things harder on yourself or with any other colleague you’re sharing results with?
Interactions among fixed factors default
There is another key default to keep in mind. GLM will automatically create interactions between any and all variables you specify as Fixed Factors.
If you put 5 variables in Fixed Factors, you’ll get a lot of interactions. SPSS will automatically create all 2-way, 3-way, 4-way, and even a 5-way interaction among those 5 variables.
That’s a lot of interactions.
In contrast, GLM doesn’t create by default any interactions between Covariates or between Covariates and Fixed Factors.
So you may find you have more interactions than you wanted among your categorical predictors. And fewer interactions than you wanted among numerical predictors.
There is no reason to use the default. You can override it quite easily.
Just click on the Model button. Then choose “Custom Model.” You can then choose which interactions you do, or don’t, want in the model.
If you’re using SPSS syntax, simply add the interactions you want to the /Design subcommand.
So think about which interactions you want in the model. And take a look at whether your variables are already dummy coded.
